Abstract: An electronic device performs one or more maneuvers in an environment including a second electronic device in accordance with some embodiments. In some embodiments, the electronic device predicts a change in acceleration of the second device caused by the first device performing a maneuver and performs the maneuver if the predicted change in acceleration satisfies one or more criteria and forgoes the maneuver if the predicted change in acceleration does not satisfy the one or more criteria. In some embodiments, while performing a maneuver as part of a movement algorithm of the electronic device, the electronic device senses data indicative of a change in acceleration of a second electronic device and updates the movement algorithm in accordance with the change in acceleration of the second electronic device satisfying one or more second criteria.

Abstract: A graph representation of a tactical map representing a plurality of static components of an environment of a vehicle is generated. Nodes of the graph represent static components, and edges represent relationships between multiple static components. Different edge types are used to indicate respective relationship semantics among the static components. Individual nodes are represented as having the same number and types of edges in the graph. Using the graph as input to a neural network based model, a set of results is obtained. A motion control directive based at least in part on the results is transmitted to a motion-control subsystem of the vehicle.

Abstract: A graph representation of a tactical map representing a plurality of static components of an environment of a vehicle is generated. Nodes of the graph represent static components, and edges represent relationships between multiple static components. Different edge types are used to indicate respective relationship semantics among the static components. Individual nodes are represented as having the same number and types of edges in the graph. Using the graph as input to a neural network based model, a set of results is obtained. A motion control directive based at least in part on the results is transmitted to a motion-control subsystem of the vehicle.

Abstract: A set of actions corresponding to a particular state of the environment of a vehicle is identified. A respective encoding is generated for different actions of the set, using elements such as distinct colors to distinguish attributes such as target lane segments. Using the encodings as inputs to respective instances of a machine learning model, respective value metrics are estimated for each of the actions. One or more motion-control directives to implement a particular action selected using the value metrics are transmitted to motion-control subsystems of the vehicle.